Usages of Semantic Web Services Technologies in IoT Ecosystems and its Impact in Services Delivery: A survey

Internet of things (IoT) has begun to emerge in our daily life through the huge number of smart services provided by the devices that deploy around us.  Vague and uncertainty in attributes that using in describing services, different levels of quality of each service and the limitation in capabilities of IoT devices are affect and hinder the process of discovering or selecting services.   The services in IoT need to be well described to enable users to receive their services that relevant to their query. This survey will investigate the most popular semantic services models and explore the use of these models in enhancing services discovery and services selection in IoT domain. Furthermore, the survey will investigate the evaluation metrics used by each study and compare the results that they obtained.&nbsp

A service broker for Intercloud computing

This thesis aims at assisting users in finding the most suitable Cloud resources taking into account their functional and non-functional SLA requirements. A key feature of the work is a Cloud service broker acting as mediator between consumers and Clouds. The research involves the implementation and evaluation of two SLA-aware match-making algorithms by use of a simulation environment. The work investigates also the optimal deployment of Multi-Cloud workflows on Intercloud environments

QoS-A ware service selection for web service composition

Composition of web services is one of the flexible and easiest approaches for creating composite services that fulfill complex tasks. Together with providing convenience in creation of new software applications, service composition has various challenges. One of them is the satisfaction of user-defined Quality of Service (QoS) requirements while selecting services for a composition. Load balancing issue is another challenge as uncontrolled workload may lead to violation of service providers’ QoS declarations. This thesis work proposes a QoS aware method for optimum service composition while taking into account load balancing. M/M/C queuing model is utilized for the individual services to determine sojourn time distribution for possible compositions. Percentile of the execution time, price and availability are considered as QoS parameters. Proposed algorithm selects the optimum composition according to QoS constraints and utility provided by the services. The performance of the method is evaluated by custom simulation software and is compared to two other methods, random selection and average execution timebased optimal service selection.M.S. - Master of Scienc

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience